Storing data and associated metadata in a dispersed storage network

ABSTRACT

A method begins by a processing module generating metadata for a data object. The method continues by a first disperse storage error encoding the metadata to produce a set of metadata slices. The method continues by partitioning the data to produce a plurality of data segments. The method continues by a second disperse storage error encoding the plurality of data segments to produce a plurality of sets of encoded data slices. The method continues by applying a distributed agreement protocol function to a data object identifier for the data object to produce ranked scoring information with regards to a plurality of storage sets. The method continues by selecting a storage set of the plurality of storage sets based on the ranked scoring information. The method continues by facilitating storage of the set of metadata slices and the plurality of sets of encoded data slices in the selected storage set.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present U.S. Utility Patent Application claims priority pursuant to35 U.S.C. §119(e) to U.S. Provisional Application No. 62/199,816,entitled “STORING DATA AND ASSOCIATED METADATA IN A DISPERSED STORAGENETWORK,” filed Jul. 31, 2015, which is hereby incorporated herein byreference in its entirety and made part of the present U.S. UtilityPatent Application for all purposes.

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

Not applicable

INCORPORATION-BY-REFERENCE OF MATERIAL SUBMITTED ON A COMPACT DISC

Not applicable

BACKGROUND OF THE INVENTION

Technical Field of the Invention

This invention relates generally to computer networks and moreparticularly to dispersing error encoded data.

Description of Related Art

Computing devices are known to communicate data, process data, and/orstore data. Such computing devices range from wireless smart phones,laptops, tablets, personal computers (PC), work stations, and video gamedevices, to data centers that support millions of web searches, stocktrades, or on-line purchases every day. In general, a computing deviceincludes a central processing unit (CPU), a memory system, userinput/output interfaces, peripheral device interfaces, and aninterconnecting bus structure.

As is further known, a computer may effectively extend its CPU by using“cloud computing” to perform one or more computing functions (e.g., aservice, an application, an algorithm, an arithmetic logic function,etc.) on behalf of the computer. Further, for large services,applications, and/or functions, cloud computing may be performed bymultiple cloud computing resources in a distributed manner to improvethe response time for completion of the service, application, and/orfunction. For example, Hadoop is an open source software framework thatsupports distributed applications enabling application execution bythousands of computers.

In addition to cloud computing, a computer may use “cloud storage” aspart of its memory system. As is known, cloud storage enables a user,via its computer, to store files, applications, etc. on an Internetstorage system. The Internet storage system may include a RAID(redundant array of independent disks) system and/or a dispersed storagesystem that uses an error correction scheme to encode data for storage.

Large distributed storage sets may incur delay when retrieving, forexample video. Reducing these delays can improve immediacy of storeddata access and subsequent interaction with the retrieved data (e.g.,playback of the video).

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWING(S)

FIG. 1 is a schematic block diagram of an embodiment of a dispersed ordistributed storage network (DSN) in accordance with the presentinvention;

FIG. 2 is a schematic block diagram of an embodiment of a computing corein accordance with the present invention;

FIG. 3 is a schematic block diagram of an example of dispersed storageerror encoding of data in accordance with the present invention;

FIG. 4 is a schematic block diagram of a generic example of an errorencoding function in accordance with the present invention;

FIG. 5 is a schematic block diagram of a specific example of an errorencoding function in accordance with the present invention;

FIG. 6 is a schematic block diagram of an example of a slice name of anencoded data slice (EDS) in accordance with the present invention;

FIG. 7 is a schematic block diagram of an example of dispersed storageerror decoding of data in accordance with the present invention;

FIG. 8 is a schematic block diagram of a generic example of an errordecoding function in accordance with the present invention;

FIG. 9 is a schematic block diagram of an embodiment of metadatadispersed storage in accordance with the present invention;

FIG. 9A is a schematic block diagram of another embodiment of metadatadispersed storage in accordance with the present invention; and

FIG. 9B is a flowchart illustrating an example of metadata dispersedstorage in accordance with the present invention.

DETAILED DESCRIPTION OF THE INVENTION

FIG. 1 is a schematic block diagram of an embodiment of a dispersed, ordistributed, storage network (DSN) 10 that includes a plurality ofcomputing devices 12-16, a managing unit 18, an integrity processingunit 20, and a DSN memory 22. The components of the DSN 10 are coupledto a network 24, which may include one or more wireless and/or wirelined communication systems; one or more non-public intranet systemsand/or public interne systems; and/or one or more local area networks(LAN) and/or wide area networks (WAN).

The DSN memory 22 includes a plurality of storage units 36 that may belocated at geographically different sites (e.g., one in Chicago, one inMilwaukee, etc.), at a common site, or a combination thereof. Forexample, if the DSN memory 22 includes eight storage units 36, eachstorage unit is located at a different site. As another example, if theDSN memory 22 includes eight storage units 36, all eight storage unitsare located at the same site. As yet another example, if the DSN memory22 includes eight storage units 36, a first pair of storage units are ata first common site, a second pair of storage units are at a secondcommon site, a third pair of storage units are at a third common site,and a fourth pair of storage units are at a fourth common site. Notethat a DSN memory 22 may include more or less than eight storage units36. Further note that each storage unit 36 includes a computing core (asshown in FIG. 2, or components thereof) and a plurality of memorydevices for storing dispersed error encoded data.

Each of the computing devices 12-16, the managing unit 18, and theintegrity processing unit 20 include a computing core 26, which includesnetwork interfaces 30-33. Computing devices 12-16 may each be a portablecomputing device and/or a fixed computing device. A portable computingdevice may be a social networking device, a gaming device, a cell phone,a smart phone, a digital assistant, a digital music player, a digitalvideo player, a laptop computer, a handheld computer, a tablet, a videogame controller, and/or any other portable device that includes acomputing core. A fixed computing device may be a computer (PC), acomputer server, a cable set-top box, a satellite receiver, a televisionset, a printer, a fax machine, home entertainment equipment, a videogame console, and/or any type of home or office computing equipment.Note that each of the managing unit 18 and the integrity processing unit20 may be separate computing devices, may be a common computing device,and/or may be integrated into one or more of the computing devices 12-16and/or into one or more of the storage units 36.

Each interface 30, 32, and 33 includes software and hardware to supportone or more communication links via the network 24 indirectly and/ordirectly. For example, interface 30 supports a communication link (e.g.,wired, wireless, direct, via a LAN, via the network 24, etc.) betweencomputing devices 14 and 16. As another example, interface 32 supportscommunication links (e.g., a wired connection, a wireless connection, aLAN connection, and/or any other type of connection to/from the network24) between computing devices 12 & 16 and the DSN memory 22. As yetanother example, interface 33 supports a communication link for each ofthe managing unit 18 and the integrity processing unit 20 to the network24.

Computing devices 12 and 16 include a dispersed storage (DS) clientmodule 34, which enables the computing device to dispersed storage errorencode and decode data as subsequently described with reference to oneor more of FIGS. 3-8. In this example embodiment, computing device 16functions as a dispersed storage processing agent for computing device14. In this role, computing device 16 dispersed storage error encodesand decodes data on behalf of computing device 14. With the use ofdispersed storage error encoding and decoding, the DSN 10 is tolerant ofa significant number of storage unit failures (the number of failures isbased on parameters of the dispersed storage error encoding function)without loss of data and without the need for a redundant or backupcopies of the data. Further, the DSN 10 stores data for an indefiniteperiod of time without data loss and in a secure manner (e.g., thesystem is very resistant to unauthorized attempts at accessing thedata).

In operation, the managing unit 18 performs DS management services. Forexample, the managing unit 18 establishes distributed data storageparameters (e.g., vault creation, distributed storage parameters,security parameters, billing information, user profile information,etc.) for computing devices 12-14 individually or as part of a group ofuser devices. As a specific example, the managing unit 18 coordinatescreation of a vault (e.g., a virtual memory block associated with aportion of an overall namespace of the DSN) within the DSTN memory 22for a user device, a group of devices, or for public access andestablishes per vault dispersed storage (DS) error encoding parametersfor a vault. The managing unit 18 facilitates storage of DS errorencoding parameters for each vault by updating registry information ofthe DSN 10, where the registry information may be stored in the DSNmemory 22, a computing device 12-16, the managing unit 18, and/or theintegrity processing unit 20.

The DSN managing unit 18 creates and stores user profile information(e.g., an access control list (ACL)) in local memory and/or withinmemory of the DSN memory 22. The user profile information includesauthentication information, permissions, and/or the security parameters.The security parameters may include encryption/decryption scheme, one ormore encryption keys, key generation scheme, and/or dataencoding/decoding scheme.

The DSN managing unit 18 creates billing information for a particularuser, a user group, a vault access, public vault access, etc. Forinstance, the DSTN managing unit 18 tracks the number of times a useraccesses a non-public vault and/or public vaults, which can be used togenerate per-access billing information. In another instance, the DSTNmanaging unit 18 tracks the amount of data stored and/or retrieved by auser device and/or a user group, which can be used to generateper-data-amount billing information.

As another example, the managing unit 18 performs network operations,network administration, and/or network maintenance. Network operationsincludes authenticating user data allocation requests (e.g., read and/orwrite requests), managing creation of vaults, establishingauthentication credentials for user devices, adding/deleting components(e.g., user devices, storage units, and/or computing devices with a DSclient module 34) to/from the DSN 10, and/or establishing authenticationcredentials for the storage units 36. Network administration includesmonitoring devices and/or units for failures, maintaining vaultinformation, determining device and/or unit activation status,determining device and/or unit loading, and/or determining any othersystem level operation that affects the performance level of the DSN 10.Network maintenance includes facilitating replacing, upgrading,repairing, and/or expanding a device and/or unit of the DSN 10.

The integrity processing unit 20 performs rebuilding of ‘bad’ or missingencoded data slices. At a high level, the integrity processing unit 20performs rebuilding by periodically attempting to retrieve/list encodeddata slices, and/or slice names of the encoded data slices, from the DSNmemory 22. For retrieved encoded slices, they are checked for errors dueto data corruption, outdated version, etc. If a slice includes an error,it is flagged as a ‘bad’ slice. For encoded data slices that were notreceived and/or not listed, they are flagged as missing slices. Badand/or missing slices are subsequently rebuilt using other retrievedencoded data slices that are deemed to be good slices to produce rebuiltslices. The rebuilt slices are stored in the DSTN memory 22.

FIG. 2 is a schematic block diagram of an embodiment of a computing core26 that includes a processing module 50, a memory controller 52, mainmemory 54, a video graphics processing unit 55, an input/output (IO)controller 56, a peripheral component interconnect (PCI) interface 58,an IO interface module 60, at least one IO device interface module 62, aread only memory (ROM) basic input output system (BIOS) 64, and one ormore memory interface modules. The one or more memory interfacemodule(s) includes one or more of a universal serial bus (USB) interfacemodule 66, a host bus adapter (HBA) interface module 68, a networkinterface module 70, a flash interface module 72, a hard drive interfacemodule 74, and a DSN interface module 76.

The DSN interface module 76 functions to mimic a conventional operatingsystem (OS) file system interface (e.g., network file system (NFS),flash file system (FFS), disk file system (DFS), file transfer protocol(FTP), web-based distributed authoring and versioning (WebDAV), etc.)and/or a block memory interface (e.g., small computer system interface(SCSI), internet small computer system interface (iSCSI), etc.). The DSNinterface module 76 and/or the network interface module 70 may functionas one or more of the interface 30-33 of FIG. 1. Note that the IO deviceinterface module 62 and/or the memory interface modules 66-76 may becollectively or individually referred to as IO ports.

FIG. 3 is a schematic block diagram of an example of dispersed storageerror encoding of data. When a computing device 12 or 16 has data tostore it disperse storage error encodes the data in accordance with adispersed storage error encoding process based on dispersed storageerror encoding parameters. The dispersed storage error encodingparameters include an encoding function (e.g., information dispersalalgorithm, Reed-Solomon, Cauchy Reed-Solomon, systematic encoding,non-systematic encoding, on-line codes, etc.), a data segmentingprotocol (e.g., data segment size, fixed, variable, etc.), and per datasegment encoding values. The per data segment encoding values include atotal, or pillar width, number (T) of encoded data slices per encodingof a data segment i.e., in a set of encoded data slices); a decodethreshold number (D) of encoded data slices of a set of encoded dataslices that are needed to recover the data segment; a read thresholdnumber (R) of encoded data slices to indicate a number of encoded dataslices per set to be read from storage for decoding of the data segment;and/or a write threshold number (W) to indicate a number of encoded dataslices per set that must be accurately stored before the encoded datasegment is deemed to have been properly stored. The dispersed storageerror encoding parameters may further include slicing information (e.g.,the number of encoded data slices that will be created for each datasegment) and/or slice security information (e.g., per encoded data sliceencryption, compression, integrity checksum, etc.).

In the present example, Cauchy Reed-Solomon has been selected as theencoding function (a generic example is shown in FIG. 4 and a specificexample is shown in FIG. 5); the data segmenting protocol is to dividethe data object into fixed sized data segments; and the per data segmentencoding values include: a pillar width of 5, a decode threshold of 3, aread threshold of 4, and a write threshold of 4. In accordance with thedata segmenting protocol, the computing device 12 or 16 divides the data(e.g., a file (e.g., text, video, audio, etc.), a data object, or otherdata arrangement) into a plurality of fixed sized data segments (e.g., 1through Y of a fixed size in range of Kilo-bytes to Tera-bytes or more).The number of data segments created is dependent of the size of the dataand the data segmenting protocol.

The computing device 12 or 16 then disperse storage error encodes a datasegment using the selected encoding function (e.g., Cauchy Reed-Solomon)to produce a set of encoded data slices. FIG. 4 illustrates a genericCauchy Reed-Solomon encoding function, which includes an encoding matrix(EM), a data matrix (DM), and a coded matrix (CM). The size of theencoding matrix (EM) is dependent on the pillar width number (T) and thedecode threshold number (D) of selected per data segment encodingvalues. To produce the data matrix (DM), the data segment is dividedinto a plurality of data blocks and the data blocks are arranged into Dnumber of rows with Z data blocks per row. Note that Z is a function ofthe number of data blocks created from the data segment and the decodethreshold number (D). The coded matrix is produced by matrix multiplyingthe data matrix by the encoding matrix.

FIG. 5 illustrates a specific example of Cauchy Reed-Solomon encodingwith a pillar number (T) of five and decode threshold number of three.In this example, a first data segment is divided into twelve data blocks(D1-D12). The coded matrix includes five rows of coded data blocks,where the first row of X11-X14 corresponds to a first encoded data slice(EDS 1_1), the second row of X21-X24 corresponds to a second encodeddata slice (EDS 2_1), the third row of X31-X34 corresponds to a thirdencoded data slice (EDS 3_1), the fourth row of X41-X44 corresponds to afourth encoded data slice (EDS 4_1), and the fifth row of X51-X54corresponds to a fifth encoded data slice (EDS 5_1). Note that thesecond number of the EDS designation corresponds to the data segmentnumber.

Returning to the discussion of FIG. 3, the computing device also createsa slice name (SN) for each encoded data slice (EDS) in the set ofencoded data slices. A typical format for a slice name 60 is shown inFIG. 6. As shown, the slice name (SN) 60 includes a pillar number of theencoded data slice (e.g., one of 1-T), a data segment number (e.g., oneof 1-Y), a vault identifier (ID), a data object identifier (ID), and mayfurther include revision level information of the encoded data slices.The slice name functions as, at least part of, a DSN address for theencoded data slice for storage and retrieval from the DSN memory 22.

As a result of encoding, the computing device 12 or 16 produces aplurality of sets of encoded data slices, which are provided with theirrespective slice names to the storage units for storage. As shown, thefirst set of encoded data slices includes EDS 1_1 through EDS 5_1 andthe first set of slice names includes SN 1_1 through SN 5_1 and the lastset of encoded data slices includes EDS 1_Y through EDS 5_Y and the lastset of slice names includes SN 1_Y through SN 5_Y.

FIG. 7 is a schematic block diagram of an example of dispersed storageerror decoding of a data object that was dispersed storage error encodedand stored in the example of FIG. 4. In this example, the computingdevice 12 or 16 retrieves from the storage units at least the decodethreshold number of encoded data slices per data segment. As a specificexample, the computing device retrieves a read threshold number ofencoded data slices.

To recover a data segment from a decode threshold number of encoded dataslices, the computing device uses a decoding function as shown in FIG.8. As shown, the decoding function is essentially an inverse of theencoding function of FIG. 4. The coded matrix includes a decodethreshold number of rows (e.g., three in this example) and the decodingmatrix in an inversion of the encoding matrix that includes thecorresponding rows of the coded matrix. For example, if the coded matrixincludes rows 1, 2, and 4, the encoding matrix is reduced to rows 1, 2,and 4, and then inverted to produce the decoding matrix.

FIG. 9 is a schematic block diagram of an embodiment of metadatadispersed storage. Embedded Objects are metadata objects which have theentirety of the content of the object stored together within metadata inthe same segment (data source). They save one RTT (round trip time-timefor a small packet to travel from client to server and back; also knownas response time) and one IO (input/output), and hence are especiallyuseful for storing small objects. Traditionally, embedded objects havebeen reserved only for small objects, since all metadata was stored in adeterministic location in generation 0. Since the DAP (distributedagreement protocol) adds expandability to generation 0, there is nolonger a reason to not store all objects as embedded objects, and so allobjects, regardless of size, may be stored with the metadata objectcontaining some part of the object data. This can amortize the time tofirst byte (TTFB), since some fixed quantity of data (e.g., 250 KB) ofthe object may be contained in the metadata itself Further, by makingthe amount of data kept in the metadata header small, it further reducesthe RTT for very small objects (e.g., not having to read an entire 4 MBsegment before accessing the first byte).

To further maximize IO capability, the first segment may not use thesame IDA (information dispersal algorithm) threshold, but may use areduced threshold, or even a threshold of “1”, such that reads of smallobjects or of metadata consume only one IOP (Input/Output Operations PerSecond) rather than an IDA (information dispersal algorithms) thresholdnumber of IOPs across the DSN memory. This strategy thereby minimizesTTFB for small objects because an embedded metadata segment is smaller,it minimizes TTFB by not requiring two round trips to access the contentfor larger objects, and increases IOPS for accessing single-segmentsmall objects by using a reduced threshold.

FIG. 9 includes the distributed storage and task (DST) processing unit16 of FIG. 1, the network 24 of FIG. 1, and at least two storage sets1-2. Alternatively, the DSN may include more than two storage sets. TheDST processing unit 16 includes a processing module 84, a dispersedstorage (DS) error encoding 1, a grouping selector 114, a DS errorencoding 2, and a data partition 110. Each storage set includes a set ofDST execution (EX) units 1-n. Each DST execution unit may be implementedutilizing the DST execution unit 36 of FIG. 1. Hereafter, each DSTexecution unit may be interchangeably referred to as a storage unit anda storage set may be interchangeably referred to as a set of storageunits. The DSN functions to store data and metadata associated with thedata in at least one storage set of the at least two storage sets 1-2.

An example of operation of the storing of the data and the metadata, theprocessing module 84 generates metadata 1 (e.g., a data object size,access rights, a data type indicator, etc.) for a received data object1.

The DS error encoding 1 dispersed storage error encodes the metadata 1utilizing first dispersal parameters (e.g., a first informationdispersal algorithm (IDA) width, a first decode threshold number, etc.)to produce a set of metadata slices 1-n. For example, the DS errorencoding 1 dispersed storage error encodes the metadata 1 to produce nidentical copies (e.g., metadata slices 1-n) of the metadata 1 when theIDA width is 1 and the decode threshold number is 1.

The data partitioning 110 partitions the received data object 1 toproduce data segments 1-S in accordance with a data segmentationapproach (e.g., text and size, segment size ramping up, segment sizeramping down), where a first segment includes fewer bytes than remainingsegments (to keep the metadata slices small after the merge below).

For each remaining data segment, the DS error encoding 2 dispersedstorage error encodes the remaining data segments to produce a setencoded data slices (DSLC) 1-n utilizing second parameters (e.g., 16/10encoding). That is, the DS error encoding 2 dispersed storage errorencodes each data segment to produce a set of encoded data slices 1-n(DSLC 1-n), of a plurality of S sets of encoded data slices, utilizingsecond dispersal parameters (e.g., IDA width of 16 and a decodethreshold number of 10).

The grouping selector 114 facilitates storage of the set of metadataslices and the remaining sets of encoded data slices in the storage set.The grouping selector 114 obtains ranked scoring information 1 from adecentralized agreement module (not shown) for each of the at least twostorage sets 1-2 based on an identifier (e.g., a source name) of thedata object 1. For example, for each storage set, the decentralizedagreement module performs a distributed agreement protocol function onthe identifier of the data object 1 using the identifier of the storageset and a weighting factor associated with the storage set to produce ascore of a plurality of scores of the ranked scoring information 1.

Having obtained the rank scoring information 1, the grouping selector114 selects a storage set of the at least two storage sets 1-2 based onthe rank scoring information 1. For example, the grouping selector 114selects a storage set associated with a highest score the plurality ofscores. For instance, the grouping selector 114 selects the storage set1 when the storage set 1 is associated with the highest score of theplurality of scores.

Having selected the storage set, the grouping selector 114 facilitatesstorage of the set of metadata slices and the plurality of sets ofencoded data slices in the selected storage set. For example, thegrouping selector 114 sends, via the network 24, the set of metadataslices 1-n (MSLC1-n) to the DST execution units 1-n for storage, sends,via the network 24, a first set of encoded data slices (e.g., DSLC1-1,DSCL 2-1, etc. through DSLC n-1) to the DST execution units 1-n forstorage, sends, via the network 24, a second set of encoded data slices(e.g., DSLC1-2, DSCL 2-2, etc. through DSLC n-2) to the DST executionunits 1-n for storage, etc.

As shown in FIG. 9A, subsequent accessing of any one of the metadataslices reproduces the metadata and at least a first portion of the dataobject. Having the metadata and first portion of the data can be veryvaluable, especially when the data is video and the 1st portion canstart an output video stream while the rest is read from the DSN.Therefore, instead of typical 16/10 coding this embodiment uses 1/1coding on the metadata/slice 1 (encoder & decoder #1) so the videostreaming output can start as soon as a first metadata slice isreceived. Retrieval is performed in reverse of operations and steps asshown in FIGS. 9 and 9B.

FIG. 9B is a flowchart illustrating an example of metadata dispersedstorage. In particular, a method is presented for use in conjunctionwith one or more functions and features described in conjunction withFIGS. 1-9A, and also FIG. 9B. The method begins at step 900 where aprocessing module (e.g., of a distributed storage and task (DST)processing unit) of a computing device of one or more computing devicesof a dispersed storage network generates metadata for a data object. Thegenerating includes analyzing one or more of the data object and a dataobject identifier of the data object to produce the metadata. The methodcontinues at the step 902 where the processing module dispersed storageerror encodes the metadata to produce a set of metadata slices. Forexample, the processing module dispersed storage error encodes themetadata utilizing first dispersal parameters to produce the set ofmetadata slices.

The method continues at step 904, where the processing module partitionsthe data to produce a plurality of data segments. For the example, theprocessing module partitions the data in accordance with a datasegmentation approach to produce the plurality of data segments. Themethod continues at step 906 where the processing module dispersedstorage error encodes the plurality of data segments to produce aplurality of sets of encoded data slices. For example, the processingmodule dispersed storage error encodes each data segment utilizingsecond dispersal parameters to produce a set of encoded data slices ofthe plurality of sets of encoded data slices.

The method continues at step 908 where the processing module applies adistributed agreement protocol function to the data object identifierfor the data object to produce ranked scoring information with regardsto a plurality of storage sets. For example, for each storage set, theprocessing module performs the distributed agreement protocol functionon the data object identifier using an identifier of the storage set anda weighting factor associated with the storage set to produce a score ofa plurality of scores of the rank scoring information.

The method continues at step 910 where the processing module selects astorage set of the plurality of storage sets based on the ranked scoringinformation. For example, the processing module identifies a storage setassociated with a highest score of the plurality of scores. The methodcontinues at step 912 where the processing module facilitates storage ofthe set of metadata slices and the plurality of sets of encoded dataslices in the selected storage set. For example, for each storage unitof the selected storage set, the processing module issues a write slicerequest to the storage unit, where the write slice request includes andassociated metadata slice of the set of metadata slices and a pluralityof associated encoded data slices of each set of encoded data slices(e.g., of a common pillar associated with the storage unit).

The method described above in conjunction with the computing device andthe storage units can alternatively be performed by other modules of thedispersed storage network or by other devices. For example, anycombination of a first module, a second module, a third module, a fourthmodule, etc. of the computing device and the storage units may performthe method described above. In addition, at least one memory section(e.g., a first memory section, a second memory section, a third memorysection, a fourth memory section, a fifth memory section, a sixth memorysection, etc. of a non-transitory computer readable storage medium) thatstores operational instructions can, when executed by one or moreprocessing modules of one or more computing devices and/or by thestorage units of the dispersed storage network (DSN), cause the one ormore computing devices and/or the storage units to perform any or all ofthe method steps described above.

It is noted that terminologies as may be used herein such as bit stream,stream, signal sequence, etc. (or their equivalents) have been usedinterchangeably to describe digital information whose contentcorresponds to any of a number of desired types (e.g., data, video,speech, audio, etc. any of which may generally be referred to as‘data’).

As may be used herein, the terms “substantially” and “approximately”provides an industry-accepted tolerance for its corresponding termand/or relativity between items. Such an industry-accepted toleranceranges from less than one percent to fifty percent and corresponds to,but is not limited to, component values, integrated circuit processvariations, temperature variations, rise and fall times, and/or thermalnoise. Such relativity between items ranges from a difference of a fewpercent to magnitude differences. As may also be used herein, theterm(s) “configured to”, “operably coupled to”, “coupled to”, and/or“coupling” includes direct coupling between items and/or indirectcoupling between items via an intervening item (e.g., an item includes,but is not limited to, a component, an element, a circuit, and/or amodule) where, for an example of indirect coupling, the intervening itemdoes not modify the information of a signal but may adjust its currentlevel, voltage level, and/or power level. As may further be used herein,inferred coupling (i.e., where one element is coupled to another elementby inference) includes direct and indirect coupling between two items inthe same manner as “coupled to”. As may even further be used herein, theterm “configured to”, “operable to”, “coupled to”, or “operably coupledto” indicates that an item includes one or more of power connections,input(s), output(s), etc., to perform, when activated, one or more itscorresponding functions and may further include inferred coupling to oneor more other items. As may still further be used herein, the term“associated with”, includes direct and/or indirect coupling of separateitems and/or one item being embedded within another item.

As may be used herein, the term “compares favorably”, indicates that acomparison between two or more items, signals, etc., provides a desiredrelationship. For example, when the desired relationship is that signal1 has a greater magnitude than signal 2, a favorable comparison may beachieved when the magnitude of signal 1 is greater than that of signal 2or when the magnitude of signal 2 is less than that of signal 1. As maybe used herein, the term “compares unfavorably”, indicates that acomparison between two or more items, signals, etc., fails to providethe desired relationship.

As may also be used herein, the terms “processing module”, “processingcircuit”, “processor”, and/or “processing unit” may be a singleprocessing device or a plurality of processing devices. Such aprocessing device may be a microprocessor, micro-controller, digitalsignal processor, microcomputer, central processing unit, fieldprogrammable gate array, programmable logic device, state machine, logiccircuitry, analog circuitry, digital circuitry, and/or any device thatmanipulates signals (analog and/or digital) based on hard coding of thecircuitry and/or operational instructions. The processing module,module, processing circuit, and/or processing unit may be, or furtherinclude, memory and/or an integrated memory element, which may be asingle memory device, a plurality of memory devices, and/or embeddedcircuitry of another processing module, module, processing circuit,and/or processing unit. Such a memory device may be a read-only memory,random access memory, volatile memory, non-volatile memory, staticmemory, dynamic memory, flash memory, cache memory, and/or any devicethat stores digital information. Note that if the processing module,module, processing circuit, and/or processing unit includes more thanone processing device, the processing devices may be centrally located(e.g., directly coupled together via a wired and/or wireless busstructure) or may be distributedly located (e.g., cloud computing viaindirect coupling via a local area network and/or a wide area network).Further note that if the processing module, module, processing circuit,and/or processing unit implements one or more of its functions via astate machine, analog circuitry, digital circuitry, and/or logiccircuitry, the memory and/or memory element storing the correspondingoperational instructions may be embedded within, or external to, thecircuitry comprising the state machine, analog circuitry, digitalcircuitry, and/or logic circuitry. Still further note that, the memoryelement may store, and the processing module, module, processingcircuit, and/or processing unit executes, hard coded and/or operationalinstructions corresponding to at least some of the steps and/orfunctions illustrated in one or more of the Figures. Such a memorydevice or memory element can be included in an article of manufacture.

One or more embodiments have been described above with the aid of methodsteps illustrating the performance of specified functions andrelationships thereof. The boundaries and sequence of these functionalbuilding blocks and method steps have been arbitrarily defined hereinfor convenience of description. Alternate boundaries and sequences canbe defined so long as the specified functions and relationships areappropriately performed. Any such alternate boundaries or sequences arethus within the scope and spirit of the claims. Further, the boundariesof these functional building blocks have been arbitrarily defined forconvenience of description. Alternate boundaries could be defined aslong as the certain significant functions are appropriately performed.Similarly, flow diagram blocks may also have been arbitrarily definedherein to illustrate certain significant functionality.

To the extent used, the flow diagram block boundaries and sequence couldhave been defined otherwise and still perform the certain significantfunctionality. Such alternate definitions of both functional buildingblocks and flow diagram blocks and sequences are thus within the scopeand spirit of the claims. One of average skill in the art will alsorecognize that the functional building blocks, and other illustrativeblocks, modules and components herein, can be implemented as illustratedor by discrete components, application specific integrated circuits,processors executing appropriate software and the like or anycombination thereof

In addition, a flow diagram may include a “start” and/or “continue”indication. The “start” and “continue” indications reflect that thesteps presented can optionally be incorporated in or otherwise used inconjunction with other routines. In this context, “start” indicates thebeginning of the first step presented and may be preceded by otheractivities not specifically shown. Further, the “continue” indicationreflects that the steps presented may be performed multiple times and/ormay be succeeded by other activities not specifically shown. Further,while a flow diagram indicates a particular ordering of steps, otherorderings are likewise possible provided that the principles ofcausality are maintained.

The one or more embodiments are used herein to illustrate one or moreaspects, one or more features, one or more concepts, and/or one or moreexamples. A physical embodiment of an apparatus, an article ofmanufacture, a machine, and/or of a process may include one or more ofthe aspects, features, concepts, examples, etc. described with referenceto one or more of the embodiments discussed herein. Further, from figureto figure, the embodiments may incorporate the same or similarly namedfunctions, steps, modules, etc. that may use the same or differentreference numbers and, as such, the functions, steps, modules, etc. maybe the same or similar functions, steps, modules, etc. or differentones.

Unless specifically stated to the contra, signals to, from, and/orbetween elements in a figure of any of the figures presented herein maybe analog or digital, continuous time or discrete time, and single-endedor differential. For instance, if a signal path is shown as asingle-ended path, it also represents a differential signal path.Similarly, if a signal path is shown as a differential path, it alsorepresents a single-ended signal path. While one or more particulararchitectures are described herein, other architectures can likewise beimplemented that use one or more data buses not expressly shown, directconnectivity between elements, and/or indirect coupling between otherelements as recognized by one of average skill in the art.

The term “module” is used in the description of one or more of theembodiments. A module implements one or more functions via a device suchas a processor or other processing device or other hardware that mayinclude or operate in association with a memory that stores operationalinstructions. A module may operate independently and/or in conjunctionwith software and/or firmware. As also used herein, a module may containone or more sub-modules, each of which may be one or more modules.

As may further be used herein, a computer readable memory includes oneor more memory elements. A memory element may be a separate memorydevice, multiple memory devices, or a set of memory locations within amemory device. Such a memory device may be a read-only memory, randomaccess memory, volatile memory, non-volatile memory, static memory,dynamic memory, flash memory, cache memory, and/or any device thatstores digital information. The memory device may be in a form a solidstate memory, a hard drive memory, cloud memory, thumb drive, servermemory, computing device memory, and/or other physical medium forstoring digital information.

While particular combinations of various functions and features of theone or more embodiments have been expressly described herein, othercombinations of these features and functions are likewise possible. Thepresent disclosure is not limited by the particular examples disclosedherein and expressly incorporates these other combinations.

What is claimed is:
 1. A method for execution by one or more processingmodules of one or more computing devices of a dispersed storage network(DSN), the method comprises: generating metadata for a data object;first disperse storage error encoding the metadata to produce a set ofmetadata slices; partitioning the data object to produce a plurality ofdata segments; second disperse storage error encoding the plurality ofdata segments to produce a plurality of sets of encoded data slices;applying a distributed agreement protocol function to a data objectidentifier for the data object to produce ranked scoring informationwith regards to a plurality of storage sets; selecting a storage set ofthe plurality of storage sets based on the ranked scoring information;and facilitating storage of the set of metadata slices and the pluralityof sets of encoded data slices in the selected storage set.
 2. Themethod of claim 1, wherein the generating metadata includes analyzingone or more of the data object and a data object identifier of the dataobject to produce the metadata.
 3. The method of claim 1, wherein thefirst disperse storage error encoding includes utilizing first dispersalparameters to store metadata in each storage unit.
 4. The method ofclaim 3, wherein the first dispersal parameters to store metadata ineach storage unit are 1 unit wide by 1 decode threshold.
 5. The methodof claim 3, wherein the second disperse storage error encoding includesutilize second dispersal parameters.
 6. The method of claim 1, whereinthe partitioning is in accordance with a data segmentation approach. 7.The method of claim 6, wherein the data segmentation approach includesany of: fixed size, ramping up size, or ramping down size.
 8. The methodof claim 1, wherein the applying a distributed agreement protocolfunction, includes, for each storage set, performing the distributedagreement protocol function on the data object identifier using anidentifier of the storage set and a weighting value of the storage setto produce a score, rank the scores to produce the ranked scoringinformation.
 9. The method of claim 1, wherein the selecting includesselecting a storage set associated with a highest score of the rankedscoring information.
 10. The method of claim 1 further comprising, foreach storage unit of the selected storage set, issuing a write slicerequest to the storage unit, where the write slice request includes anassociated metadata slice of the set of metadata slices and a pluralityof associated encoded data slices of each set of encoded data slices.11. The method of claim 10, wherein the plurality of associated encodeddata slices of each set of encoded data slices includes a common pillar.12. A computing device of a group of computing devices of a dispersedstorage network (DSN), the computing device comprises: an interface; alocal memory; and a processing module operably coupled to the interfaceand the local memory, wherein the processing module functions to:generate metadata for a data object; first disperse storage error encodethe metadata to produce a set of metadata slices; partition the dataobject to produce a plurality of data segments; second disperse storageerror encode the plurality of data segments to produce a plurality ofsets of encoded data slices; apply a distributed agreement protocolfunction to a data object identifier for the data object to produceranked scoring information with regards to a plurality of storage sets;select a storage set of the plurality of storage sets based on theranked scoring information; and facilitate storage of the set ofmetadata slices and the plurality of sets of encoded data slices in theselected storage set.
 13. The computing device of claim 12, wherein thegenerating metadata includes analyzing one or more of the data objectand a data object identifier of the data object to produce the metadata.14. The computing device of claim 12, wherein the first disperse storageerror encoding includes utilizing first dispersal parameters to storemetadata in each storage unit.
 15. The computing device of claim 14,wherein the first dispersal parameters to store metadata in each storageunit are 1 unit wide by 1 decode threshold.
 16. The computing device ofclaim 14, wherein the second disperse storage error encoding includesutilize second dispersal parameters.
 17. The computing device of claim12, wherein the partitioning is in accordance with a data segmentationapproach.
 18. The computing device of claim 17, wherein the datasegmentation approach includes any of: fixed size, ramping up size, orramping down size.
 19. The computing device of claim 12, wherein theapplying a distributed agreement protocol function, includes, for eachstorage set, performing the distributed agreement protocol function onthe data object identifier using an identifier of the storage set and aweighting value of the storage set to produce a score, rank the scoresto produce the ranked scoring information.
 20. The computing device ofclaim 12 further comprising, for each storage unit of the selectedstorage set, issuing a write slice request to the storage unit, wherethe write slice request includes an associated metadata slice of the setof metadata slices and a plurality of associated encoded data slices ofeach set of encoded data slices.